ANATOMIC MORPHOLOGIC ANALYSIS OF MR BRAIN IMAGES

MR 脑图像的解剖形态分析

• 批准号: 2891984
• 负责人: David Nelson Kennedy
• 金额: $ 37.48万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-30 至 2002-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2891984
• 关键词: bioimaging /biomedical imaging; brain disorder diagnosis; brain metabolism; brain neoplasms; cerebrovascular disorder diagnosis; classification; computer assisted diagnosis; computer program /software; computer simulation; computer system design /evaluation; diagnosis design /evaluation; functional magnetic resonance imaging; human data; image processing; neoplasm /cancer diagnosis; neuroanatomy; stroke

DESCRIPTION (Adapted from applicant's abstract): The morphology of the human brain is exceptionally complex; reflecting a myriad of inextricably intertwined systems of neuronal cell bodies, axons and other components. Groupings of neural components which share common structural or functional properties comprise the structural and functional neuroanatomic framework of the brain. Characterization of the morphologic properties of the brain and its component parts by state-of-the-art MRI is well suited to permit a quantitative study of the parameters relevant to the structural and functional makeup of the human brain in vivo. The goal of this project is to continue development of tools and methods for the precise quantitative analysis of brain morphology in health and disease, and to disseminate the tools and results of the application of these tools to the neuroscience community as a whole. Specifically, this project will: (1) extend previously developed pixel segmentation and morphological quantification methods to time-domain (functional MRI) and tensor-valued (diffusion-weighted MRI), continuing efforts to develop a unified neuroanatomic segmentation framework, and transition these tools to clinical applications on a routinely available software platform; (2) characterize shape and shape change metrics in normal subjects and pathological patient populations; and (3) dissemination of segmentation tools and comparison methods, as well as the results of image segmentation and volumetric analysis to the community as a whole using the World Wide Web. This proposal continues to take advantage of several unique aspects which distinguishes it from other related work. First, a unified framework for segmentation and classification in support of a neurologically-based anatomic morphology has begun to emerge. Second, this unified framework incorporates the multispectral nature of MRI data. Third, this framework intrinsically includes estimates of the resulting underlying uncertainty associated with the segmentation and classification process which supports a rational assessment of sensitivity of a given method. Fourth, this approach expands upon traditional "static" image analysis by incorporation of shape-based analysis for anomaly detection. In addition, several clinical application areas were identified which, in addition to fostering enhanced analytic capabilities to studies in these areas, permits optimization of operational efficiency in the resulting analysis. Specifically, the segmentation, classification and shape analysis of MRI data in patients with Huntington's Disease, stroke, and central nervous system neoplasms, as well as the appropriate normative subjects, provide a vital testbed for the evaluation of the clinical utility of these morphological analysis techniques.

描述（改编自申请人的摘要）： 人类大脑异常复杂，反映了无数错综复杂的 神经元细胞体、轴突和其他成分的交织系统。 具有共同结构或功能的神经成分的分组 属性包括结构和功能的神经解剖框架， 大脑 脑的形态学特性的表征， 其组成部分通过最先进的MRI非常适合于允许 定量研究有关的参数的结构和 人体大脑的功能组成。 这个项目的目标是 继续开发工具和方法， 健康和疾病中的脑形态分析，并传播 工具和这些工具在神经科学中的应用结果 整个社区。 具体而言，该项目将：（1）扩大 先前开发的像素分割和形态量化 时域（功能性MRI）和张量值 （扩散加权MRI），继续努力开发一个统一的 神经解剖分割框架，并将这些工具过渡到临床 在常规可用的软件平台上的应用程序;（2）表征 正常受试者和病理患者中的形状和形状变化度量 （3）传播细分工具和比较 方法，以及图像分割和体积的结果 使用万维网对整个社区进行分析。 这项建议继续利用了几个独特的方面， 区别于其他相关工作。 第一，统一的框架， 分割和分类，以支持基于神经学的 解剖形态学开始出现。 第二，这个统一的框架 结合了MRI数据的多光谱性质。 第三，这个框架 本质上包括对由此产生的潜在不确定性的估计 与分割和分类过程相关联， 合理评估给定方法的灵敏度。 第四，这种方法 扩展了传统的“静态”图像分析， 用于异常检测的基于形状的分析。 此外，一些临床 确定了应用领域，除了促进增强 分析能力，在这些领域的研究，允许优化 分析结果中的操作效率。 具体而言是 MRI数据的分割、分类和形状分析 亨廷顿病、中风和中枢神经系统肿瘤，以及 作为适当的规范性科目，提供了一个重要的试验台， 评价这些形态学分析的临床实用性 技术.